Predicting the risk of postoperative gastrointestinal bleeding in patients with Type A aortic dissection based on an interpretable machine learning model by Lin Li, Xing Yang, Wei Guo, Wenxian Wu, Meixia Guo, Huanhuan Li, Xueyan Wang, Siyu Che

“…Predictors were screened using LASSO regression, and four ML algorithms—Random Forest (RF), K-nearest neighbor (KNN), Support Vector Machines (SVM), and Decision Tree (DT)—were employed to construct models for predicting postoperative GIB risk. …”
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Assessment of prostate cancer aggressiveness through the combined analysis of prostate MRI and 2.5D deep learning models by Yalei Wang, Yuqing Xin, Baoqi Zhang, Fuqiang Pan, Xu Li, Manman Zhang, Yushan Yuan, Lei Zhang, Peiqi Ma, Bo Guan, Yang Zhang

“…Models were constructed using the LightGBM algorithm: a radiomic feature model, a deep learning feature model, and a combined model integrating radiomic and deep learning features. …”
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Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis by Ibrahim Mohammadzadeh, Bardia Hajikarimloo, Behnaz Niroomand, Nasira Faizi, Pooya Eini, Mohammad Amin Habibi, Alireza Mohseni, Mohammadmahdi Sabahi, Abdulrahman Albakr, Michael Karsy, Hamid Borghei-Razavi

“…For the comparison between Logistic Regression (LR) and non-LR algorithms, LR-based algorithms exhibited numerically higher AUC and sensitivity; however, these differences were not statistically significant. …”
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Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning by Su Zhang, Weitao Hu, Yuchao Tang, Hongjie Lin, Xiaoqing Chen

“…Three machine learning algorithms were applied to DE-IRGs to screen for hub DE-IRGs. …”
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Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study by Yimei Zhang, Yu Wang, Jingran Yang, Qinglan Li, Min Zhou, Jiafei Lu, Qiulan Hu, Fang Ma

“…Eighteen features screened through a previous umbrella review informed the models. …”
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Identification of developmental and reproductive toxicity of biocides in consumer products using ToxCast bioassays data and machine learning models by Donghyeon Kim, Siyeol Ahn, Jinhee Choi

“…This study aimed to identify ToxCast bioassays relevant to DART and develop machine learning models to screen biocides in consumer products for their DART potential. …”
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Construction and analysis of a prognostic risk scoring model for gastric cancer anoikis-related genes based on LASSO regression by Ai CHEN, Xiaowei CHEN, Yanan WANG, Xiaobing SHEN

“…ResultsSix key ARGs (VCAN, FEN1, BRIP1, CNTN1, P3H2, DUSP1) were screened out based on LASSO regression analysis, and a prognostic risk scoring model was constructed. …”
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Construction and Validation of a Hospital Mortality Risk Model for Advanced Elderly Patients with Heart Failure Based on Machine Learning by Shang S, Wei M, Lv H, Liang X, Lu Y, Tang B

“…Shuai Shang,1,2,* Meng Wei,1,2,* Huasheng Lv,1,2,* Xiaoyan Liang,1,2 Yanmei Lu,1,2 Baopeng Tang1,2 1Department of Cardiac Pacing and Electrophysiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China; 2Xinjiang Key Laboratory of Cardiac Electrophysiology and Remodeling, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Baopeng Tang, Department of Cardiac Pacing and Electrophysiology, Xinjiang Key Laboratory of Cardiac Electrophysiology and Remodeling, The First Affiliated Hospital of Xinjiang Medical University, No. 137, South Liyushan Road, Xinshi Zone, Urumqi, People’s Republic of China, Email tangbaopeng1111@163.com Yanmei Lu, Department of Cardiac Pacing and Electrophysiology, Xinjiang Key Laboratory of Cardiac Electrophysiology and Remodeling, The First Affiliated Hospital of Xinjiang Medical University, No. 137, South Liyushan Road, Xinshi Zone, Urumqi, People’s Republic of China, Email gracy@189.cnPurpose: This study aimed to develop and validate a model based on machine learning algorithms to predict the risk of in-hospital death among advanced elderly patients with Heart Failure (HF).Methods: A total of 4580 advanced elderly patients who were admitted to the hospital and diagnosed with HF from May 2012 to September 2023 were included in this study, among whom 552 cases (12.5%) died. …”
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Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles. by Ali Shojaie, Alexandra Jauhiainen, Michael Kallitsis, George Michailidis

“…The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data. …”
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Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning. by Ji-Ying Chen, Wu-Jie Chen, Zhi-Ying Zhu, Shi Xu, Li-Lan Huang, Wen-Qing Tan, Yong-Gang Zhang, Yan-Li Zhao

“…Three machine learning models, logistic regression, random forest, and support vector machine, showed that screened biomarkers had better classification ability and effect. …”
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Assessment of food toxicology by Alexander Gosslau

“…Integration of food toxicology data obtained throughout biochemical and cell-based in vitro, animal in vivo and human clinical settings has enabled the establishment of alternative, highly predictable in silico models. These systems utilize a combination of complex in vitro cell-based models with computer-based algorithms. …”
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Diabetes and Cataracts Development—Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study by Adriana Ivanescu, Simona Popescu, Adina Braha, Bogdan Timar, Teodora Sorescu, Sandra Lazar, Romulus Timar, Laura Gaita

“…<i>Conclusions:</i> These findings suggest that diabetes may impact the type of cataract that develops, with CC being notably more prevalent in diabetic patients. This has important implications for screening and management strategies for cataract formation in diabetic populations.…”
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Development of Electronic Nose as a Complementary Screening Tool for Breath Testing in Colorectal Cancer by Chih-Dao Chen, Yong-Xiang Zheng, Heng-Fu Lin, Hsiao-Yu Yang

“…We then used machine learning algorithms to develop predictive models and provided the estimated accuracy and reliability of the breath testing. (3) Results: We enrolled 77 patients, with 40 cases and 37 controls. …”
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MF-ShipNet: a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing images by Jianfeng Li, Yibing Yang, Liutong Yang, Yang Zhao, Qinghua Luo, Chenxu Wang

“…To solve this problem, this paper proposes a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing images. …”
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Improvement of the Diagnostics of the Fetus Heart Anomalies During a Routine Screening Ultrasound Examination by Markin L., Medvjedjeva O.

“…In our opinion, the prenatal detection of congenital heart defects strongly depends on the algorithm of conducting a fetal heart study in the screening regimen. …”
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Short-Term Photovoltaic Power Combined Prediction Based on Feature Screening and Weight Optimization by Liqing Geng, Yadong Yang, Genghuang Yang, Yongfeng Zheng, Xiaocong Liu

“…Aiming at the problem of low prediction accuracy caused by the intermittent and fluctuating characteristics of photovoltaic power, a short-term photovoltaic power combined prediction method based on feature screening and weight optimization is proposed. Firstly, K-means is used to cluster the photovoltaic power; Secondly, CEEMDAN is used to decompose photovoltaic power and wavelet decomposition is used to decompose irradiance, and sample entropy and K-means are used to reconstruct each component of photovoltaic power into high, intermediate, and low frequency terms; Then, Spearman&#x2019;s correlation coefficient is used to calculate the correlation between each meteorological factor and the decomposed irradiance component and the high, intermediate, and low frequency terms of photovoltaic power, and the feature selection is carried out; Then, CNN-BiLSTM-Attention is used to predict the high frequency term, LSTM is used to predict the intermediate frequency and low frequency terms, and the results are superimposed to obtain the preliminary prediction value; Finally, the dung beetle algorithm is used to optimize the weights of the initial prediction values of the training set of high, intermediate, and low frequency terms, and the optimal weight is substituted into the test set to obtain the final prediction result. …”
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Artificial intelligence in primary aldosteronism: current achievements and future challenges by Yisi Xu, Benjin Liu, Xuqi Huang, Xudong Guo, Ning Suo, Shaobo Jiang, Hanbo Wang

“…Recent advances in artificial intelligence (AI) are reshaping the diagnostic and therapeutic of primary aldosteronism (PA). For screening, machine learning models integrate multidimensional data to improve the efficiency of PA detection, facilitating large-scale population screening. …”
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Design and evaluation of screening and self-care (mobile) application for oral and dental problems and emergencies by Sedighe Sadat Hashemikamangar, Aidin Sooratgar, Mina Khayamzadeh, Shayan Momeni, Ali Asghar Safaei, Behnaz Behniafar

“…Results: The evaluation results showed that more than 90 % of specialists had a positive attitude towards application effectiveness. …”
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Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007–2018 by Efrain Riveros Perez, Bibiana Avella-Molano

“…This study is innovative in its integration of machine learning algorithms to predict type 2 diabetes based solely on non-invasive, easily accessible lifestyle and anthropometric variables, demonstrating the potential of data-driven models for early risk assessment without requiring laboratory tests. …”
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Developing a logistic regression model to predict spontaneous preterm birth from maternal socio-demographic and obstetric history at initial pregnancy registration by Brenda F. Narice, Mariam Labib, Mengxiao Wang, Victoria Byrne, Joanna Shepherd, Z. Q. Lang, Dilly OC Anumba

“…Abstract Background Current predictive machine learning techniques for spontaneous preterm birth heavily rely on a history of previous preterm birth and/or costly techniques such as fetal fibronectin and ultrasound measurement of cervical length to the disadvantage of those considered at low risk and/or those who have no access to more expensive screening tools. Aims and objectives We aimed to develop a predictive model for spontaneous preterm delivery < 37 weeks using socio-demographic and clinical data readily available at booking -an approach which could be suitable for all women regardless of their previous obstetric history. …”
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